A data compression method comprises encoding groups of data items by generating, for each group, header data comprising h-bits and a plurality of body portions each comprising b-bits and each body portion corresponding to a data item in the group. The value of h may be fixed for all groups and the value of b is fixed within a group, wherein the header data for a group comprises an indication of b for the body portions of that group. In various examples, b=0 and so there are no body portions. In examples where b is not equal to zero, a body data field is generated for each group by interleaving bits from the body portions corresponding to data items in the group. The resultant encoded data block, comprising the header data and, where present, the body data field can be written to memory.

Examples may include racks for a data center and sleds for the racks, the sleds arranged to house physical resources for the data center. The sleds can house physical resources and heat sinks thermally coupled to the physical resources. The physical resources are arranged on the sleds and the heat sinks are configured so as to limit thermal shadowing between physical resources to reduce interference with airflow provided by fans of the racks.

Aspects include copying a plurality of input data into a buffer of a processor configured to perform speculatively executing pipelined streaming of the input data. A bit counter maintains a difference in a number of input bits from the input data entering a pipeline of the processor and a number of the input bits consumed in the pipeline. The pipeline is flushed based on detecting an error. A portion of the input data is recirculated from the buffer into the pipeline based on a value of the bit counter.

Technologies for allocating resources of a set of managed nodes to workloads based on resource utilization phase residencies include an orchestrator server to receive resource allocation objective data and determine an assignment of a set of workloads among the managed nodes. The orchestrator server is further to receive telemetry data from the managed nodes, determine, as a function of the telemetry data, phase residency data, determine, as a function of at least the phase residency data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing the achievement of any of the other resource allocation objectives, and apply the adjustment to the assignments of the workloads among the managed nodes as the workloads are performed.

Examples may include sleds for a rack in a data center including physical compute resources and memory for the physical compute resources. The memory can be disaggregated, or organized into first level and second level memory. A first sled can comprise the physical compute resources and a first set of physical memory resources while a second sled can comprise a second set of physical memory resources. The first set of physical memory resources can be coupled to the physical compute resources via a local interface while the second set of physical memory resources can be coupled to the physical compute resources via a fabric.

A system and method to manage a non-universal encoder and a universal encoder for compression of data include receiving the data. The data includes symbols. The method also includes subdividing the data into a first set of data blocks and a second set of data blocks and generating a non-universal encoder using the first set of data blocks. The non-universal encoder includes first codes. Each of the first codes corresponds to one of the symbols in the first set of data blocks only and at least one of the first codes includes fewer bits than the symbol corresponding to the at least one of the first codes. The method further includes compressing the second set of data blocks using at least the non-universal encoder.

Technologies for dynamically allocating resources within a self-managed node include a self-managed node to receive quality of service objective data indicative of a performance objective of one or more workloads assigned to the self-managed node. Each workload includes one or more tasks. The self-managed node is also to execute the one or more tasks to perform the one or more workloads, obtain telemetry data as the workloads are performed, determine, as a function of the telemetry data, an adjustment to the allocation of resources among the workloads to satisfy the performance objective, and apply the determined adjustment as the workloads are performed by the self-managed node. Other embodiments are also described and claimed.

Embodiments are generally directed apparatuses, methods, techniques and so forth to receive a sled manifest comprising identifiers for physical resources of a sled, receive results of an authentication and validation operations performed to authenticate and validate the physical resources of the sled, determine whether the results of the authentication and validation operations indicate the physical resources are authenticate or not authenticate. Further and in response to the determination that the results indicate the physical resources are authenticated, permit the physical resources to process a workload, and in response to the determination that the results indicate the physical resources are not authenticated, prevent the physical resources from processing the workload.

Technologies for a low-latency interface with data storage of a storage sled in a data center are disclosed. In the illustrative embodiment, a storage sled stores metadata including the location of data in a storage device in low-latency non-volatile memory. When accessing data, the storage sled may access the metadata on the low-latency non-volatile memory and then, based on the location determined by the access to the metadata, access the location of the data in the storage device. Such an approach results in only one access to the data storage in order to read the data instead of two.

Methods and systems for improving coding decoding efficiency of video by providing a syntax modeler, a buffer, and a decoder. The syntax modeler may associate a first sequence of symbols with syntax elements. The buffer may store tables, each represented by a symbol in the first sequence, and each used to associate a respective symbol in a second sequence of symbols with encoded data. The decoder decodes the data into a bitstream using the second sequence retrieved from a table.